Deep Mapping, Creating Maps With Cross-related Context

Imaging and Scanning Information

• Aerial Photography and Satellite Imagery
  • Time-series of overhead images (from early aerial photos in the 1920s-30s, to satellite images from the 1970s onward, and modern high-res imagery) allow direct observation of changes – construction of new suburbs, infill development, changes in land cover (green to paved), etc.
• Historical Maps and Surveys
  • Old cadastral maps, land surveys, and plats that show parcel layouts, lot lines, and ownership. These are fundamental for aligning historical data to modern coordinates.
  • They might reveal vanished streets or old property extents that set the stage for later development.
• Street-Level Photography
  • Modern street photos
  • Collections of street photos (like historic streetscape archives).
  • These let the system see facade changes or business signage changes.
• 3D Scans and Building Models
  • Lidar scans, photogrammetry, or BIM models of buildings and blocks across time.
  • For example, terrestrial laser scans of historic buildings can document architectural details and any changes (restoration, deterioration).
• Citywide 3D model 
  • Allow volumetric comparisons – how the skyline grew, where open space was filled in, etc. Furthermore, historical if available, or reconstructed from maps
• Architectural drawings/paintings
  • Old drawings of the city that may show how the buildings were in the past.

Surveying Information

• City topology surveying information
• Cadastral surveying

City Level Planning, Zoning

• Plat maps/Cadastre/Cadastral maps
• Urban Structure
  • Arrangement of land use in urban areas.

Block and Building Level Mapping

• Floor Plans
  • Floor plans for the different buildings, parks, etc. in the city.
• House Plans
  • Dimensions, materials, layouts, installation methods and techniques.
• Elevations
  • Building façades, the appearance of a building from the exterior.

Legal information 

• Property Registries 
  • Ownership history of the different buildings.

• Legal and Technical Documents
  • Blueprints, building permit records, and zoning documents (already identified by the user) provide the technical narrative behind physical changes.
  • For instance, a series of building permits on a lot can show it evolving from a two-story house to a ten-story apartment over a century.
  • Coupling these with the above data types ensures the researcher has both the “why” (permit, legal reason) and the “what” (visual evidence of the change).

Roads, Transport and Logistics

• Road Networks
  • Public roads, Highways, private roads, government roads, road type (transit, logistics, etc)
  • For example, the construction of an urban highway in the 1960s might have diverted traffic from old surface streets (and split neighborhoods – data the researcher can use to understand socio-economic impacts), while the later removal of a highway or addition of a bypass would again reshuffle flow.
  • Road Network Changes 
    • Geospatial data on when new roads or highways were built, or when streets were closed/converted (e.g. pedestrian malls). These events drastically alter traffic patterns.
• Public Transport Maps
  • Historic and current public transport routes/lines
  • Recording historical usage, different public transport methods, peak hours, frequencies, speeds, etc.
• Ports, airports, etc.
  • Location, traffic, seasons, peak hours, peak traffic.
• Traffic Volume, Patterns and Congestion
  • Historical traffic count data on major roads​, including Average Annual Daily Traffic (AADT) counts, can map how vehicle flow has changed. For example, we might see a downtown street peaking in car traffic in the 1980s and then declining after pedestrianization in the 2000s.
  • Many cities and state DOTs publish traffic count maps by year, which can be layered to show rising or falling congestion. (Open data portals sometimes provide decades of traffic counts for key intersections or highway segments.)
• Pedestrian Foot Traffic
  • Data on footfall in commercial areas (from old manual counts or modern sensors/mobile data). While historical pedestrian counts are rare, we can infer foot traffic from things like the presence of streetcar lines (pre-automobile era) or later from pedestrianization initiatives.
  • In recent years, smart city sensors and smartphone data have begun to record pedestrian flows, so a researcher could use that for contemporary patterns and project backward or forward.
• Bicycle Infrastructure and Usage
  • Maps of bike lanes, bike paths, and cycling infrastructure build-out over time. Many cities only introduced dedicated bike lanes in the late 20th century; mapping their expansion shows changing priorities.
  • Where available, bike share program data or cycling traffic counters by location can illustrate the growth of cycling popularity in certain neighborhoods.
• Parking and Vehicle Data
  • Locations of parking lots/garages over time (some downtowns converted old buildings to parking or vice versa) and possibly vehicle registration data by zip code. This can signal car dependency in various areas
  • E.g. a rise in car ownership in suburbs vs. decline in center city – complementing traffic data.

Utilities, Services

• Public utilities
  • Electrification, water distribution, gas distribution, etc. to the different areas of the city.
  • When information about the implementation and availability of public utilities and services across the decades becomes available, the researcher can get a good picture about how the city prioritized the development its different areas and zones.
• Utilities Mapping
  • The mapping, description and specification of the different pipes, wires, going though the city.

Communications Infrastructure 

• Telephone and Telegraph Networks
  • Historical maps of telephone lines, telegraph routes, and exchange locations.
  • Early 20th-century phone books sometimes include maps of exchange areas. Seeing which neighborhoods got telephone service first (and which lagged) offers insight into historical wealth and influence patterns.
• Broadband Internet and Fiber Optics
  • Data on high-speed internet availability by area over time. Governments and ISPs often release maps of broadband coverage.
  • Using archived versions of such maps, one can plot when each part of the city got broadband, cable, or fiber-optic upgrades. (E.g. perhaps downtown had cable internet by the 1990s, but outlying areas waited till the 2010s for broadband – a gap indicating the “digital divide.”)​.
• Cellular Towers and Wireless Coverage
  • Locations and activation dates of cell towers, along with the progression from 2G to 5G networks.
  • A timeline of cell tower installations on a map shows the spread of mobile connectivity. Additionally, coverage heatmaps for different eras (from analog cell coverage in the 1980s to 4G/5G coverage today) highlight which areas had strong or poor signal, affecting technology adoption.
• Public Wi-Fi and Communication Hubs
  • Data on public internet kiosks, Wi-Fi hotspot zones, or community internet centers.
  • For example, a city might install municipal Wi-Fi downtown in 2005, then expand it to parks by 2015 – mapping these rollouts can answer when and where free internet access was offered.
• Telecommunications Infrastructure
  • Other data like radio/TV broadcast tower locations, cable TV franchise areas, and even telecom conduit maps beneath streets. These show the “invisible” network.
  • For instance, knowledge of where fiber-optic backbone lines run could explain why certain tech companies cluster in one area (access to better connectivity).
• Legacy Communications Infrastructure
  • This category collects information about legacy/traditional communications infrastructure.
  • Post offices, mail boxes, etc.
• Historic Communications Infrastructure
  • The location/availability of historic/obsolete communications infrastructure.
  • Horse mail rest posts and inns for the riders, carriage mail/transport maintenance shops and rests, etc.

Industry

• Industrial and Zoning Data
  • Records of designated industrial areas, their peak usage, and eventual redevelopment. City zoning maps, paired with actual business operation data show how strictly zones were used as intended.
  • For instance, a zone once full of auto plants might be rezoned to mixed-use after the plants closed, bringing in offices or housing – business data confirms these transitions.

History and Events Information

• Remarkable/Historic Buildings History
  • Locations, descriptions, annotations of buildings considered either historic and remarkable.
  • This include recording any changes, renovations, damages, inhabitants, etc.
• General Building History
  • Locations, descriptions, annotations of the different buildings of the city
  • This include recording any changes, renovations, damages, inhabitants, etc.
• Events
  • A description of different events of varying degrees of importance that took place in the city, including mapping them within the city.
  • Important visits by heads of state or very famous people.
  • Big cultural events.
  • Historic events.

Emergency and Disaster Mapping 

• Fire Histories
  • Records of major urban fires (like Chicago 1871, San Francisco 1906) and smaller fire incidents mapped by location.
  • These show which areas were most vulnerable to conflagration and often led to stricter building regulations in those zones.
  • Modern GIS layers might include fire risk maps for neighborhoods (especially in wildland-urban interface areas) and historical fire insurance maps indicating building materials and densities.
• Flood Zones and Water Events
  • Floodplain maps and flood incident records over time. Many cities have FEMA Flood Insurance Rate Maps that delineate 100-year and 500-year flood zones – tracking revisions of these maps shows how flood risk expands with climate change or new drainage projects.
  • Additionally, archives of actual flood events (e.g. inundation extent of hurricanes or river floods in specific years) can be layered to see which districts repeatedly suffer.
• Earthquake and Disaster Damage
  • For seismically active regions, data on historical earthquake impact by neighborhood (shake intensity maps, lists of buildings collapsed by quake, etc.). Similarly, storm damage maps (tornado paths, hurricane wind damage zones) highlight the spatial footprint of disasters.
  • These data, combined with building information, help identify resilient vs. vulnerable structures.
• Emergency Response and Evacuation
  • Plans and drills for disasters, such as designated evacuation routes, shelter locations, or emergency service coverage areas, provide a temporal dimension when compared across years.
  • For example, before and after a major disaster, how did the city change its evacuation route map or the distribution of emergency shelters? This indicates lessons learned and evolving strategy.
• Recovery and Reconstruction
  • Data on post-disaster recovery projects (e.g. maps of federal disaster aid by neighborhood, or areas rebuilt after a fire/flood) show how the urban landscape was altered in response.
  • A researcher could use this to answer questions like “How did the downtown area change after the 1931 flood?” by referencing building permit surges or new flood defenses built subsequently.

Census and Demographic History

• Population Counts and Density
  • Decennial census data at the block or tract level, showing population numbers, density, and households. Over time, this reveals growth corridors, urban densification or depopulation (e.g. inner-city population decline in mid-20th century, followed by late-20th-century suburban boom).
• Demographic Composition
  • Data on age distribution, gender, race/ethnicity, and family structure by area.
  • This allows mapping of shifts such as the post-WWII Baby Boom (rapid growth of young families in certain suburbs) or later aging of the population in those same neighborhoods.
  • Ethnic enclave formation and migration can be tracked (for example, seeing a “Little Italy” transition into a Chinatown or gentrification changing the racial makeup of a district).
• Socioeconomic Indicators
  • Income levels, poverty rates, educational attainment, and employment by neighborhood. Such data (often from census long-form surveys or modern ACS data) illustrate economic stratification in the city map.
  • For instance, one could visualize how median income by block changed with the arrival of new industries or how unemployment spiked in certain areas after factory closures.
• Migration and Housing Data
  • Statistics on where people are moving from/to within the city.
  • This might include data on new residents by area (e.g. an influx of young professionals downtown in the 2000s) or vacancy rates and housing occupancy changes.
  • Home ownership vs. renting rates by block can also indicate stability or turnover in communities.

Cultural History and Information

• Cultural Distribution and Patterns
  • This include: annotating, describing and mapping the areas according to wealth level, ethnic enclaves, cultural areas, etc.
• Cultures and sub-culture
  • A map of different cultural areas and sub-cultures. For example, areas with highly artistic sectors, etc.

Political and Ideological History

• Political Boundaries and Election Results
  • Voting precinct maps, city council districts, and zoning of legislative wards over time. Coupled with precinct-level election results.
  • These data reveal changing political allegiances and the impact of redistricting or annexations on neighborhoods.
• Policy Impact Areas
  • Geographic footprints of government programs or laws – e.g. urban renewal project areas, historical redistricting plans, or designated development zones.
  • Tracking these helps explain why certain areas boomed or declined after specific policies (such as subsidized housing projects or business improvement districts).

Economic History and Information  

• Cost of Living
  • The cost of living in the city and its different sub-areas through its history.
• Tax and Land Value Records
  • Historical property tax assessments, land valuation maps, and tax incentive zones. These show economic priority areas (e.g. enterprise zones) and shifts in land value.
  • For instance, New Deal-era “redlining” maps graded neighborhoods by credit risk, affecting investment for decades​.
• Economic Regulations and Ordinances
  • Locations of regulated zones like rent control districts, historic preservation areas, or special tax districts.
  • These legal designations influence development patterns and can be mapped over time to see how regulatory focus shifted across the city.
• Tourism
  • Touristic areas, touristic patterns and attractions.

Commerce and Business

• Business Directories and Licenses
  • Old city business directories, phone books (yellow pages), and trade directories list businesses by address. These can be digitized and geocoded to show which businesses occupied a building over time​.
  • For example, a single building might have housed a tailor in 1920, a department store in 1950, and a tech startup in 2020 – business listings help “flesh out the story” of what happened within those walls​
  • Such data illuminates the changing commercial character of streets and districts (e.g. an area transitioning from industrial warehouses to art galleries and cafes).
• Economic Activity Maps
  • Locations of markets, shopping centers, and major employers over time. This could include factory sites, warehouse districts, central business districts, and later tech parks or malls.
  • Mapping these highlights shifts like the decline of manufacturing zones or the rise of a new financial district.
• Commercial Real Estate and Land Use
  • Data on retail vs. office vs. residential space usage. Historical floor space inventories or building use surveys can quantify how much of a neighborhood’s area was commercial at different times.
  • Business tax receipts or chamber of commerce reports could also indicate economic hotspots and declines by area.
• Markets and Small Business Culture
  • Information on the locations of informal commerce such as street markets, food stalls, or bazaars, and how these evolved or were regulated. Cultural economic data.
  • E.g. where certain crafts or trades clustered (garment districts, meatpacking districts) – add texture to the city’s economic history.

Social and Recreational  

• Parks and Green Spaces
  • Maps of parks, playgrounds, and public gardens over time. Green space distribution is key to urban quality of life.
  • Tracking park creation, expansion, or loss (e.g. new parks in the 1900s City Beautiful movement, or parkland lost to development in later decades) can answer questions about environmental equity and recreation.
• Libraries, Museums and Civic Buildings
  • The locations of public libraries, community centers, museums, and other civic amenities. These often serve as community anchors.
  • For example, a new library opening can revitalize an area; conversely, library closures might indicate population decline. Historical data on these facilities (including photos or architectural plans) also reflects civic priorities (e.g. Carnegie libraries built in the early 20th century across various neighborhoods).
• Sports and Entertainment Venues
  • All the places people seek leisure – sports stadiums, cinemas and theaters, concert halls, nightclubs, bars, and restaurants.
  • A temporal map of movie theater locations in a city, for instance, might show the golden age of cinema with dozens of neighborhood theaters in the 1940s, dwindling to multiplexes by the 2000s. Similarly, sports venue construction (from old baseball fields to modern arenas) can be plotted, showing shifts in entertainment hubs.
• Public Plazas and Markets
  • Plazas, squares, farmer’s markets, and other open gathering spots.
  • These are often tied to cultural events (parades, protests, festivals). Data might include city event permits by location or historical photographs/newspapers noting popular gathering sites. This gives the researches context for where public life and social movements played out.
• Religious and Cultural Institutions
  • The distribution of churches, temples, mosques, and cultural community centers (like YMCAs, youth clubs, senior centers) across time.
  • These often reflect demographic changes – e.g. the rise of certain immigrant communities is marked by the establishment of new religious centers. They also double as community gathering spaces, contributing to neighborhood identity.

Public Health and Epidemiology Data

• Disease Outbreak Maps
  • A classic example is John Snow’s 1854 London cholera map, which plotted deaths and linked the outbreak to a water pump – an early triumph of spatial health analysis​.
  • Such maps show how illnesses spread through certain districts and spurred public health infrastructure (like sewers or clean water systems).
• Hospital and Clinic Records
  • Locations and founding dates of hospitals, clinics, and other medical facilities. Trends in where healthcare infrastructure expanded (or closed) reflect population needs and policy priorities (e.g. the establishment of charity hospitals in poorer quarters vs. private clinics in affluent areas).
• Pollution and Environmental Health
  • Data on air quality, water quality, and toxic sites by neighborhood. For example, pollution exposure maps (tracking smog, lead, or PM2.5 levels in different districts) show environmental disparities. Health outcome data (asthma rates, birth defects, etc.) by area can be correlated with industrial zones or traffic density.
• Public Health Interventions
  • Records of past health campaigns or crises localized in space – vaccination drives, sanitation improvements, quarantined districts during epidemics, etc. These temporal snapshots (e.g. a 19th-century cholera quarantine zone or a 2020s COVID testing site map) highlight how cities respond spatially to health needs.

Safety and Security Information

• Crime Mapping
  • Crime Incident Maps
  • Divided by types of crimes.
  • Geocoded crime reports (e.g. police blotter data or modern 911 incident databases) can be aggregated by year to show hotspots and cold spots of various crimes.
  • Historical crime mapping has roots going back to the 19th century – as early as 1829, researchers in France produced maps relating education levels to violent and property crime rates​.
  • Modern policing famously uses mapping too: the NYPD’s CompStat since 1994 mapped crimes by precinct to identify trouble areas and trends.
  • By compiling decades of crime data, one can see, for example, the drop in violent crime in many city centers from the 1990s to 2020s, or specific neighborhoods that improved/worsened.
• Accidents Mapping
  • Traffic accidents, and other types of accidents mapping.
• High Security Areas/Zones
  • Mapping areas with a heightened level of security, like for example embassies and public buildings.
• Police and Fire Infrastructure
  • Locations of police stations, fire stations, and their service boundaries over time. These show how coverage expanded as cities grew (new stations in new suburbs) or shifted with needs. Response times and coverage maps (like “ beats ” or precincts) changing can inform about public safety focus.
• Community Safety Programs
  • Data on neighborhood watch zones, CCTV camera deployments, or other security measures by area. For instance, a city might roll out a “Safe Streets” program with better lighting and cameras in high-crime areas – mapping where and when this occurred can correlate with crime reductions.
• Fire Incidents and Emergency Calls
  • Beyond large disasters, everyday public safety data like fire department incident logs or 911 call heatmaps reveal safety issues. Frequent calls in certain blocks might indicate chronic problems (e.g. building code issues or public disorder). Over years, these patterns could change with redevelopment or gentrification (often 911 calls for some categories drop as neighborhoods stabilize economically).

Education and Academic Information

• School Locations and Zones
  • Historical addresses of schools (elementary, secondary) and how school district boundaries changed over time. As cities grow, new schools open and others close or consolidate.
  • Mapping these shows shifts in population centers and educational access. (For instance, desegregation busing plans or new school construction in suburbs can be charted geographically.)
• Higher Education Campuses
  • The expansion of universities and colleges, including satellite campuses or research parks.
  • University maps and campus plans reveal how institutions physically reshape neighborhoods (e.g. expansion of a college into adjacent blocks over decades).
• School Quality and Enrollment
  • Data on school enrollment numbers, class sizes, or performance metrics by school over time.
  • When paired with location, one could see, for example, how a neighborhood’s school crowding or test scores improved/declined, possibly influencing housing demand nearby.
• Educational Demographics and Programs
  • Information on student demographics by school (racial/ethnic makeup, economic status) and special programs (magnet schools, vocational institutes) across neighborhoods.
  • This helps trace social changes – for example, waves of immigration might show up as new language programs at local schools, or the closure of trade schools might parallel decline of local industries.

• Meteorological information
  • Historic and current.
• Sound recordings
  • Sound recordings in different areas of the city.
  • Similarly, sound recordings (if available, e.g. urban soundscape archives or noise level monitoring data) could add an audio dimension – the rise in city noise over time, or changing soundscapes of neighborhoods (e.g. streetcars clanging in 1900 versus highway noise in 2000).
• Greenery
  • Plants, tree, their species and locations, tree and plant density, etc.
  • Related to: Social and Recreational >> Parks and Green Spaces